Pan-KRAS inhibitors suppress proliferation through feedback regulation in pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is currently one of the most lethal cancers worldwide. Several basic studies have confirmed that Kirsten rat sarcoma virus (KRAS) is a key driver gene for the occurrence of PDAC, and KRAS mutations have also been found in most patients in clinical studies. In this study, two pan-KRAS inhibitors, BI-2852 and BAY-293, were chosen as chemical probes to investigate their antitumor potency in PDAC. Their inhibitory effects on KRAS activation were validated in vitro and their antiproliferative potency in PDAC cell lines were profiled, with half-maximal inhibitory concentration (IC50) values of approximately 1 µM, demonstrating the therapeutic potential of pan-KRAS inhibitors in the treatment of PDAC. However, feedback regulation in the KRAS pathway weakened inhibitor activity, which was observed by a 50 times difference in BAY-293 from in vitro activity. Furthermore, pan-KRAS inhibitors effectively inhibited cell proliferation in 3D organoids cultured from PDAC patient samples; however, there were some variations between individuals. These results provide a sufficient theoretical foundation for KRAS as a clinical therapeutic target and for the application of pan-KRAS inhibitors in the treatment of PDAC, with important scientific significance in translational medicine.

Unraveling allosteric landscapes of allosterome with ASD.

Allosteric regulation is one of the most direct and efficient ways to fine-tune protein function; it is induced by the binding of a ligand at an allosteric site that is topographically distinct from an orthosteric site. The Allosteric Database (ASD, available online at http://mdl.shsmu.edu.cn/ASD) was developed ten years ago to provide comprehensive information related to allosteric regulation. In recent years, allosteric regulation has received great attention in biological research, bioengineering, and drug discovery, leading to the emergence of entire allosteric landscapes as allosteromes. To facilitate research from the perspective of the allosterome, in ASD 2019, novel features were curated as follows: (i) >10 000 potential allosteric sites of human proteins were deposited for allosteric drug discovery; (ii) 7 human allosterome maps, including protease and ion channel maps, were built to reveal allosteric evolution within families; (iii) 1312 somatic missense mutations at allosteric sites were collected from patient samples from 33 cancer types and (iv) 1493 pharmacophores extracted from allosteric sites were provided for modulator screening. Over the past ten years, the ASD has become a central resource for studying allosteric regulation and will play more important roles in both target identification and allosteric drug discovery in the future.

AlloDriver: a method for the identification and analysis of cancer driver targets.

Identifying the variants that alter protein function is a promising strategy for deciphering the biological consequences of somatic mutations during tumorigenesis, which could provide novel targets for the development of cancer therapies. Here, based on our previously developed method, we present a strategy called AlloDriver that identifies cancer driver genes/proteins as possible targets from mutations. AlloDriver utilizes structural and dynamic features to prioritize potentially functional genes/proteins in individual cancers via mapping mutations generated from clinical cancer samples to allosteric/orthosteric sites derived from three-dimensional protein structures. This strategy exhibits desirable performance in the reemergence of known cancer driver mutations and genes/proteins from clinical samples. Significantly, the practicability of AlloDriver to discover novel cancer driver proteins in head and neck squamous cell carcinoma (HNSC) was tested in a real case of human protein tyrosine phosphatase, receptor type K (PTPRK) through a L1143F driver mutation located at the allosteric site of PTPRK, which was experimentally validated by cell proliferation assay. AlloDriver is expected to help to uncover innovative molecular mechanisms of tumorigenesis by perturbing proteins and to discover novel targets based on cancer driver mutations. The AlloDriver is freely available to all users at http://mdl.shsmu.edu.cn/ALD.

Proteome-Scale Investigation of Protein Allosteric Regulation Perturbed by Somatic Mutations in 7,000 Cancer Genomes.

The allosteric regulation triggering the protein's functional activity via conformational changes is an intrinsic function of protein under many physiological and pathological conditions, including cancer. Identification of the biological effects of specific somatic variants on allosteric proteins and the phenotypes that they alter during tumor initiation and progression is a central challenge for cancer genomes in the post-genomic era. Here, we mapped more than 47,000 somatic missense mutations observed in approximately 7,000 tumor-normal matched samples across 33 cancer types into protein allosteric sites to prioritize the mutated allosteric proteins and we tested our prediction in cancer cell lines. We found that the deleterious mutations identified in cancer genomes were more significantly enriched at protein allosteric sites than tolerated mutations, suggesting a critical role for protein allosteric variants in cancer. Next, we developed a statistical approach, namely AlloDriver, and further identified 15 potential mutated allosteric proteins during pan-cancer and individual cancer-type analyses. More importantly, we experimentally confirmed that p.Pro360Ala on PDE10A played a potential oncogenic role in mediating tumorigenesis in non-small cell lung cancer (NSCLC). In summary, these findings shed light on the role of allosteric regulation during tumorigenesis and provide a useful tool for the timely development of targeted cancer therapies.

Allosteric Methods and Their Applications: Facilitating the Discovery of Allosteric Drugs and the Investigation of Allosteric Mechanisms.

Allostery, or allosteric regulation, is the phenomenon in which protein functional activity is altered by the binding of an effector at an allosteric site that is topographically distinct from the orthosteric, active site. As one of the most direct and efficient ways to regulate protein function, allostery has played a fundamental role in innumerable biological processes of all living organisms, including enzyme catalysis, signal transduction, cell metabolism, and gene transcription. It is thus considered as "the second secret of life". The abnormality of allosteric communication networks between allosteric and orthosteric sites is associated with the pathogenesis of human diseases. Allosteric modulators, by attaching to structurally diverse allosteric sites, offer the potential for differential selectivity and improved safety compared with orthosteric drugs that bind to conserved orthosteric sites. Harnessing allostery has thus been regarded as a novel strategy for drug discovery. Despite much progress having been made in the repertoire of allostery since the turn of the millennium, the identification of allosteric drugs for therapeutic targets and the elucidation of allosteric mechanisms still present substantial challenges. These challenges are derived from the difficulties in the identification of allosteric sites and mutations, the assessment of allosteric protein-modulator interactions, the screening of allosteric modulators, and the elucidation of allosteric mechanisms in biological systems. To address these issues, we have developed a panel of allosteric services for specific allosteric applications over the past decade, including (i) the creation of the Allosteric Database, with the aim of providing comprehensive allosteric information such as allosteric proteins, modulators, sites, pathways, etc., (ii) the construction of the ASBench benchmark of high-quality allosteric sites for the development of computational methods for predicting allosteric sites, (iii) the development of Allosite and AllositePro for the prediction of the location of allosteric sites in proteins, (iv) the development of the Alloscore scoring function for the evaluation of allosteric protein-modulator interactions, (v) the development of Allosterome for evolutionary analysis of query allosteric sites/modulators within the human proteome, (vi) the development of AlloDriver for the prediction of allosteric mutagenesis, and (vii) the development of AlloFinder for the virtual screening of allosteric modulators and the investigation of allosteric mechanisms. Importantly, we have validated computationally predicted allosteric sites, mutations, and modulators in the real cases of sirtuin 6, casein kinase 2α, phosphodiesterase 10A, and signal transduction and activation of transcription 3. Furthermore, our developed allosteric methods have been widely exploited by other users around the world for allosteric research. Therefore, these allosteric services are expected to expedite the discovery of allosteric drugs and the investigation of allosteric mechanisms.

AlloFinder: a strategy for allosteric modulator discovery and allosterome analyses.

Allostery tweaks innumerable biological processes and plays a fundamental role in human disease and drug discovery. Exploration of allostery has thus been regarded as a crucial requirement for research on biological mechanisms and the development of novel therapeutics. Here, based on our previously developed allosteric data and methods, we present an interactive platform called AlloFinder that identifies potential endogenous or exogenous allosteric modulators and their involvement in human allosterome. AlloFinder automatically amalgamates allosteric site identification, allosteric screening and allosteric scoring evaluation of modulator-protein complexes to identify allosteric modulators, followed by allosterome mapping analyses of predicted allosteric sites and modulators in human proteome. This web server exhibits prominent performance in the reemergence of allosteric metabolites and exogenous allosteric modulators in known allosteric proteins. Specifically, AlloFinder enables identification of allosteric metabolites for metabolic enzymes and screening of potential allosteric compounds for disease-related targets. Significantly, the feasibility of AlloFinder to discover allosteric modulators was tested in a real case of signal transduction and activation of transcription 3 (STAT3) and validated by mutagenesis and functional experiments. Collectively, AlloFinder is expected to contribute to exploration of the mechanisms of allosteric regulation between metabolites and metabolic enzymes, and to accelerate allosteric drug discovery. The AlloFinder web server is freely available to all users at http://mdl.shsmu.edu.cn/ALF/.

Are the Apo Proteins Suitable for the Rational Discovery of Allosteric Drugs?

Allosteric modulators, by targeting the less-conserved allosteric sites, represent an innovative strategy in drug discovery. These modulators have a distinctive advantage over orthosteric ligands that attach to the conserved, functional orthosteric sites. However, in structure-based drug design, it remains unclear whether allosteric protein structures determined without orthosteric ligand binding are suitable for allosteric drug screening. In this study, we performed large-scale conformational samplings of six representative allosteric proteins uncomplexed ( apo) and complexed ( holo) with orthosteric ligands to explore the effect of orthosteric site binding on the conformational dynamics of allosteric sites. The results, coupled with the redocking evaluation of allosteric modulators to their apo and holo proteins using their MD trajectories, indicated that orthosteric site binding had an effect on the dynamics of the allosteric sites and allosteric modulators preferentially bound to their holo proteins. According to the analysis data, we constructed a new correlation model for quantifying the allosteric site change driven by substrate binding to the orthosteric site. These results highlight the strong demand to select holo allosteric proteins as initial inputs in structure-based allosteric drug screening when the distance between orthosteric and allosteric sites in the protein is below 5 Å, which is expected to contribute to allosteric drug discovery.

ASD v3.0: unraveling allosteric regulation with structural mechanisms and biological networks.

Allosteric regulation, the most direct and efficient way of regulating protein function, is induced by the binding of a ligand at one site that is topographically distinct from an orthosteric site. Allosteric Database (ASD, available online at http://mdl.shsmu.edu.cn/ASD) has been developed to provide comprehensive information featuring allosteric regulation. With increasing data, fundamental questions pertaining to allostery are currently receiving more attention from the mechanism of allosteric changes in an individual protein to the entire effect of the changes in the interconnected network in the cell. Thus, the following novel features were added to this updated version: (i) structural mechanisms of more than 1600 allosteric actions were elucidated by a comparison of site structures before and after the binding of an modulator; (ii) 261 allosteric networks were identified to unveil how the allosteric action in a single protein would propagate to affect downstream proteins; (iii) two of the largest human allosteromes, protein kinases and GPCRs, were thoroughly constructed; and (iv) web interface and data organization were completely redesigned for efficient access. In addition, allosteric data have largely expanded in this update. These updates are useful for facilitating the investigation of allosteric mechanisms, dynamic networks and drug discoveries.

Alloscore: a method for predicting allosteric ligand-protein interactions.

UNLABELLED: Allosteric ligands have increasingly gained attention as potential therapeutic agents due to their higher target selectivity and lower toxicity compared with classic orthosteric ligands. Despite the great interest in the development of allosteric drugs as a new tactic in drug discovery, the understanding of the ligand-protein interactions underlying allosteric binding represents a key challenge. Herein, we introduce Alloscore, a web server that predicts the binding affinities of allosteric ligand-protein interactions. This method exhibits prominent performance in describing allosteric binding and could be useful in allosteric virtual screening and the structural optimization of allosteric agonists/antagonists. AVAILABILITY AND IMPLEMENTATION: The Alloscore server and tutorials are freely available at http://mdl.shsmu.edu.cn/alloscore CONTACT: jian.zhang@sjtu.edu.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Improved Method for the Identification and Validation of Allosteric Sites.

Allosteric regulation induced by modulators binding to different, often distant, allosteric sites allows for exquisite control of protein functional activity. The structural diversity of allosteric sites endows allosteric modulators with high selectivity and low toxicity. Targeting allosteric sites, a novel tactic in drug discovery, has garnered much attention in the scientific community, and the identification of allosteric sites has become an important component of the development of allosteric drugs. Here we present AllositePro, a method which predicts allosteric sites in proteins by combining pocket features with perturbation analysis. The performance of AllositePro is superior to that of the other currently available methods. Using AllositePro, we predicted a novel allosteric site in cyclin-dependent kinase 2 (CDK2) and validated it by site-directed mutagenesis assay. Thus, the AllositePro method provides an effective way to identify allosteric sites and could be a useful strategy for allosteric drug discovery.

ASBench: benchmarking sets for allosteric discovery.

Allostery allows for the fine-tuning of protein function. Targeting allosteric sites is gaining increasing recognition as a novel strategy in drug design. The key challenge in the discovery of allosteric sites has strongly motivated the development of computational methods and thus high-quality, publicly accessible standard data have become indispensable. Here, we report benchmarking data for experimentally determined allosteric sites through a complex process, including a 'Core set' with 235 unique allosteric sites and a 'Core-Diversity set' with 147 structurally diverse allosteric sites. These benchmarking sets can be exploited to develop efficient computational methods to predict unknown allosteric sites in proteins and reveal unique allosteric ligand-protein interactions to guide allosteric drug design.

ASD v2.0: updated content and novel features focusing on allosteric regulation.

Allostery is the most direct and efficient way for regulation of biological macromolecule function and is induced by the binding of a ligand at an allosteric site topographically distinct from the orthosteric site. AlloSteric Database (ASD, http://mdl.shsmu.edu.cn/ASD) has been developed to provide comprehensive information on allostery. Owing to the inherent high receptor selectivity and lower target-based toxicity, allosteric regulation is expected to assume a more prominent role in drug discovery and bioengineering, leading to the rapid growth of allosteric findings. In this updated version, ASD v2.0 has expanded to 1286 allosteric proteins, 565 allosteric diseases and 22 008 allosteric modulators. A total of 907 allosteric site-modulator structural complexes and >200 structural pairs of orthosteric/allosteric sites in the allosteric proteins were constructed for researchers to develop allosteric site and pathway tools in response to community demands. Up-to-date allosteric pathways were manually curated in the updated version. In addition, both the front-end and the back-end of ASD have been redesigned and enhanced to allow more efficient access. Taken together, these updates are useful for facilitating the investigation of allosteric mechanisms, allosteric target identification and allosteric drug discovery.

In silico site of metabolism prediction for human UGT-catalyzed reactions.

MOTIVATION: The human uridine diphosphate-glucuronosyltransferase enzyme family catalyzes the glucuronidation of the glycosyl group of a nucleotide sugar to an acceptor compound (substrate), which is the most common conjugation pathway that serves to protect the organism from the potential toxicity of xenobiotics. Moreover, it could affect the pharmacological profile of a drug. Therefore, it is important to identify the metabolically labile sites for glucuronidation. RESULTS: In the present study, we developed four in silico models to predict sites of glucuronidation, for four major sites of metabolism functional groups, i.e. aliphatic hydroxyl, aromatic hydroxyl, carboxylic acid or amino nitrogen, respectively. According to the mechanism of glucuronidation, a series of 'local' and 'global' molecular descriptors characterizing the atomic reactivity, bonding strength and physical-chemical properties were calculated and selected with a genetic algorithm-based feature selection approach. The constructed support vector machine classification models show good prediction performance, with the balanced accuracy ranging from 0.88 to 0.96 on test set. For further validation, our models can successfully identify 84% of experimentally observed sites of metabolisms for an external test set containing 54 molecules. AVAILABILITY AND IMPLEMENTATION: The software somugt based on our models is available at www.dddc.ac.cn/adme/jlpeng/somugt_win32.zip.

The structural basis of ATP as an allosteric modulator.

Adenosine-5'-triphosphate (ATP) is generally regarded as a substrate for energy currency and protein modification. Recent findings uncovered the allosteric function of ATP in cellular signal transduction but little is understood about this critical behavior of ATP. Through extensive analysis of ATP in solution and proteins, we found that the free ATP can exist in the compact and extended conformations in solution, and the two different conformational characteristics may be responsible for ATP to exert distinct biological functions: ATP molecules adopt both compact and extended conformations in the allosteric binding sites but conserve extended conformations in the substrate binding sites. Nudged elastic band simulations unveiled the distinct dynamic processes of ATP binding to the corresponding allosteric and substrate binding sites of uridine monophosphate kinase, and suggested that in solution ATP preferentially binds to the substrate binding sites of proteins. When the ATP molecules occupy the allosteric binding sites, the allosteric trigger from ATP to fuel allosteric communication between allosteric and functional sites is stemmed mainly from the triphosphate part of ATP, with a small number from the adenine part of ATP. Taken together, our results provide overall understanding of ATP allosteric functions responsible for regulation in biological systems.

PRDX1 Interfering Peptide Disrupts Amino Acids 70-90 of PRDX1 to Inhibit the TLR4/NF-κB Signaling Pathway and Attenuate Neuroinflammation and Ischemic Brain Injury.

Ischemic stroke ranks among the leading causes of death and disability in humans and is accompanied by motor and cognitive impairment. However, the precise mechanisms underlying injury after stroke and effective treatment strategies require further investigation. Peroxiredoxin-1 (PRDX1) triggers an extensive inflammatory cascade that plays a pivotal role in the pathology of ischemic stroke, resulting in severe brain damage from activated microglia. In the present study, we used molecular dynamics simulation and nuclear magnetic resonance to detect the interaction between PRDX1 and a specific interfering peptide. We used behavioral, morphological, and molecular experimental methods to demonstrate the effect of PRDX1-peptide on cerebral ischemia-reperfusion (I/R) in mice and to investigate the related mechanism. We found that PRDX1-peptide bound specifically to PRDX1 and improved motor and cognitive functions in I/R mice. In addition, pretreatment with PRDX1-peptide reduced the infarct area and decreased the number of apoptotic cells in the penumbra. Furthermore, PRDX1-peptide inhibited microglial activation and downregulated proinflammatory cytokines including IL-1ß, IL-6, and TNF-α through inhibition of the TLR4/NF-κB signaling pathway, thereby attenuating ischemic brain injury. Our findings clarify the precise mechanism underlying PRDX1-induced inflammation after ischemic stroke and suggest that the PRDX1-peptide can significantly alleviate the postischemic inflammatory response by interfering with PRDX1 amino acids 70-90 and thereby inhibiting the TLR4/NF-κB signaling pathway. Our study provides a theoretical basis for a new therapeutic strategy to treat ischemic stroke.

Binding sensitivity of adefovir to the polymerase from different genotypes of HBV: molecular modeling, docking and dynamics simulation studies.

AIM: To investigate the molecular mechanisms underlying the influence of DNA polymerase from different genotypes of hepatitis B virus (HBV) on the binding affinity of adefovir (ADV). METHODS: Computational approaches, including homology modeling, docking, MD simulation and MM/PBSA free energy analyses were used. RESULTS: Sequence analyses revealed that residue 238 near the binding pocket was not only a polymorphic site but also a genotype-specific site (His238 in genotype B; Asn238 in genotype C). The calculated binding free-energy supported the hypothesis that the polymerase from HBV genotype C was more sensitive to ADV than that from genotype B. By using MD simulation trajectory analysis, binding free energy decomposition and alanine scanning, some energy variation in the residues around the binding pocket was observed. Both the alanine mutations at residues 236 and 238 led to an increase of the energy difference between genotypes C and B (ΔΔG(C-B)), suggesting that these residues contributed to the genotype-associated antiviral variability with regard to the interaction with ADV. CONCLUSION: The results support the hypothesis that the HBV genotype C polymerase is more sensitive to ADV than that from genotype B. Moreover, residue N236 and the polymorphic site 238 play important roles in contributing to the higher sensitivity of genotype C over B in the interaction with ADV.

Estimation of carcinogenicity using molecular fragments tree.

Carcinogenicity is an important toxicological endpoint that poses high concern to drug discovery. In this study, we developed a method to extract structural alerts (SAs) and modulating factors of carcinogens on the basis of statistical analyses. First, the Gaston algorithm, a frequent subgraph mining method, was used to detect substructures that occurred at least six times. Then, a molecular fragments tree was built and pruned to select high-quality SAs. The p-value of the parent node in the tree and that of its children nodes were compared, and the nodes that had a higher statistical significance in binomial tests were retained. Finally, modulating factors that suppressed the toxic effects of SAs were extracted by three self-defining rules. The accuracy of the 77 SAs plus four SA/modulating factor pairs model for the training set, and the test set was 0.70 and 0.65, respectively. Our model has higher predictive ability than Benigni's model, especially in the test set. The results highlight that this method is preferable in terms of prediction accuracy, and the selected SAs are useful for prediction as well as interpretation. Moreover, our method is convenient to users in that it can extract SAs from a database using an automated and unbiased manner that does not rely on a priori knowledge of mechanism of action.

Knowledge-based scoring functions in drug design: 2. Can the knowledge base be enriched?

Fast and accurate predicting of the binding affinities of large sets of diverse protein−ligand complexes is an important, yet extremely challenging, task in drug discovery. The development of knowledge-based scoring functions exploiting structural information of known protein−ligand complexes represents a valuable contribution to such a computational prediction. In this study, we report a scoring function named IPMF that integrates additional experimental binding affinity information into the extracted potentials, on the assumption that a scoring function with the "enriched" knowledge base may achieve increased accuracy in binding affinity prediction. In our approach, the functions and atom types of PMF04 were inherited to implicitly capture binding effects that are hard to model explicitly, and a novel iteration device was designed to gradually tailor the initial potentials. We evaluated the performance of the resultant IPMF with a diverse set of 219 protein-ligand complexes and compared it with seven scoring functions commonly used in computer-aided drug design, including GLIDE, AutoDock4, VINA, PLP, LUDI, PMF, and PMF04. While the IPMF is only moderately successful in ranking native or near native conformations, it yields the lowest mean error of 1.41 log K(i)/K(d) units from measured inhibition affinities and the highest Pearson's correlation coefficient of R(p)2 0.40 for the test set. These results corroborate our initial supposition about the role of "enriched" knowledge base. With the rapid growing volume of high-quality structural and interaction data in the public domain, this work marks a positive step toward improving the accuracy of knowledge-based scoring functions in binding affinity prediction.

Fragment-based prediction of skin sensitization using recursive partitioning.

Skin sensitization is an important toxic endpoint in the risk assessment of chemicals. In this paper, structure-activity relationships analysis was performed on the skin sensitization potential of 357 compounds with local lymph node assay data. Structural fragments were extracted by GASTON (GrAph/Sequence/Tree extractiON) from the training set. Eight fragments with accuracy significantly higher than 0.73 (p<0.1) were retained to make up an indicator descriptor fragment. The fragment descriptor and eight other physicochemical descriptors closely related to the endpoint were calculated to construct the recursive partitioning tree (RP tree) for classification. The balanced accuracy of the training set, test set I, and test set II in the leave-one-out model were 0.846, 0.800, and 0.809, respectively. The results highlight that fragment-based RP tree is a preferable method for identifying skin sensitizers. Moreover, the selected fragments provide useful structural information for exploring sensitization mechanisms, and RP tree creates a graphic tree to identify the most important properties associated with skin sensitization. They can provide some guidance for designing of drugs with lower sensitization level.

In vitro biolayer interferometry analysis of acetylcholinesterase as a potential target of aryl-organophosphorus flame-retardants.

Organophosphorus flame retardants (OPFRs) have been implicated as neurotoxicants, but their potential neurotoxicity and mechanisms remain poorly understood. Herein, we investigated the neurotoxicity of selected OPFRs using zebrafish as a model organism. Environmentally relevant concentrations (3-1500 nM) of three classes of OPFRs (aryl-OPFRs, chlorinated-OPFRs, and alkyl-OPFRs) were tested in zebrafish larvae (2-144 h post-fertilisation) alongside the neurotoxic chemical chlorpyrifos (CPF) that inhibits acetylcholinesterase (AChE). Exposure to aryl-OPFRs and CPF inhibited AChE activities, while chlorinated- and alkyl-OPFRs did not inhibit these enzymes. Biolayer interferometry (BLI) was used to probe interactions between OPFRs and AChE. The association and dissociation response curves showed that, like CPF, all three selected aryl-OPFRs, triphenyl phosphate (TPHP), tricresyl phosphate (TCP) and cresyl diphenyl phosphate (CDP), bound directly to AChE. The affinity constant (KD) for TPHP, TCP, CDP and CPF was 2.18 × 10-4, 5.47 × 10-5, 1.05 × 10-4 and 1.70 × 10-5 M, respectively. In addition, molecular docking revealed that TPHP, TCP, CDP and CPF bound to AChE with glide scores of - 7.8, - 8.3, - 8.1 and - 7.3, respectively. Furthermore, the calculated binding affinity between OPFRs and AChE correlated well with the KD values measured by BLI. The present study revealed that aryl-OPFRs can act as potent AChE inhibitors, and may therefore present a significant ecological risk to aquatic organisms.